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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: new package margdistfit available on SSC |

Date |
Fri, 18 Nov 2011 17:31:04 +0100 |

On Fri, Nov 18, 2011 at 4:43 PM, Austin Nichols wrote: > This is an interesting exercise, though I would think only relevant > for ML since no theoretical distribution is assumed for OLS etc. Sure, I initially wrote it for -betafit- (available from SSC), which fits a beta distribution with ML. The main reason for including linear regression is didactic, more people are familiar with linear regression and the normal distribution than with beta regression and the beta distribution. As you remarked, there is a risk attached to that strategy in that users may over-interpret the graphs in case of linear regression. Though I do believe that even with linear regression it allows for a useful view on the data and the model in that the normal distribution is a useful baseline. Deviations from it can point to interesting, unusual, disturbing or puzzling patterns in the data. I find it often useful to know that such patterns exist in my data even though I do not need to do anything about it. > Minor points: > > 1. A parametric regression typically does not allow parameters to > change as X changes, contrary to your text describing the command: <snip> I see how what I wrote could be interpreted in the way you interpreted it. However, when wrote that I was thinking in terms of a distribution rather than regression, and the parameter in that case is the mean or standard deviation or some other parameter e.g. the scale or shape parameters in the beta-distribution and not the regression parameters. I need to make that more clear in my text. > 2. What effect do heteroskedasticity or clustering of errors have on > your examples? Must you assume i.i.d. errors? Heteroskedasticity can be accommodated if it is explicitly modeled. For example, in -betafit- one can let the variance depend on covariates by adding those covariates in the -phivar() option. When one has asked for robust standard errors (and thus also in case of clustered standard errors), one has already relaxed the distribution assumptions. So in that case the theoretical distribution with which the empricial distribution is compared only represents a useful baseline instead of a hard assumption. I have to think a bit on the consequences of clustering. > 3. The link to "helpfile" at http://www.maartenbuis.nl/software/margdistfit.html > pointing to http://repec.org/bocode/m/margdistfit.html > seems to be broken. thanks, I will look into it. Thanks for your comments, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: new package margdistfit available on SSC***From:*Maarten Buis <maartenlbuis@gmail.com>

**st: RE: new package margdistfit available on SSC***From:*"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>

**Re: st: RE: new package margdistfit available on SSC***From:*Austin Nichols <austinnichols@gmail.com>

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